Camera Self-Calibration from Two Views with a Common Direction

Yingna Su, Yingna Su, Xinnian Guo, Xinnian Guo, Yang Shen

2024

Abstract

Camera calibration is crucial for enabling accurate and robust visual perception. This paper addresses the challenge of recovering intrinsic camera parameters from two views of a planar surface, that has received limited attention due to its inherent degeneracy. For cameras equipped with Inertial Measurement Units (IMUs), such as those in smartphones and drones, the camera’s y-axes can be aligned with the gravity direction, reducing the relative orientation to a one-degree-of-freedom (1-DoF). A key insight is the general orthogonality between the ground plane and the gravity direction. Leveraging this ground plane constraint, the paper introduces new homography-based minimal solutions for camera self-calibration with a known gravity direction. we derive 2.5- and 3.5-point camera self-calibration algorithms for points in the ground plane to enable simultaneous estimation of the camera’s focal length and principal point. The paper demonstrates the practicality and efficiency of these algorithms and comparisons to existing state-of-the-art methods, confirming their reliability under various levels of noise and different camera configurations.

Paper Citation

in Harvard Style

Su Y., Guo X. and Shen Y. (2024). Camera Self-Calibration from Two Views with a Common Direction. In Proceedings of the 19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: VISAPP; ISBN 978-989-758-679-8, SciTePress, pages 680-685. DOI: 10.5220/0012438100003660

in Bibtex Style

@conference{visapp24,
author={Yingna Su and Xinnian Guo and Yang Shen},
title={Camera Self-Calibration from Two Views with a Common Direction},
booktitle={Proceedings of the 19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: VISAPP},
year={2024},
pages={680-685},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012438100003660},
isbn={978-989-758-679-8},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: VISAPP
TI - Camera Self-Calibration from Two Views with a Common Direction
SN - 978-989-758-679-8
AU - Su Y.
AU - Guo X.
AU - Shen Y.
PY - 2024
SP - 680
EP - 685
DO - 10.5220/0012438100003660
PB - SciTePress